Multi-thread low-power-consumption intelligent monitoring system based on AI processor

Abstract

The invention relates to the technical field of intelligent monitoring, in particular to a multi-thread low-power-consumption intelligent monitoring system based on an AI processor. The method has the advantages that aiming at the problems of unbalanced computing power and power consumption, high multi-task processing delay and strong hardware dependence in the prior art, the NPU module of the RK3588 processor is combined with the INT8 quantitative model, so that the power consumption is lower than 10W under the 6TOPS computing power; a dynamic multi-thread scheduling mechanism is designed, parallel processing of more than eight paths of video streams is supported through binding of a priority queue and an NPU core, and end-to-end delay is compressed to be within 200 ms; a zero-copy video stream architecture is constructed, data transfer is eliminated through memory mapping, and preprocessing time consumption is reduced by 90%; an energy efficiency control module is integrated, the NPU voltage frequency is dynamically adjusted according to the load, and the energy efficiency ratio reaches 0.83 TOPS / W; space-time alignment of multi-model reasoning results is realized by adopting a frame ID synchronization technology, and the mismatching rate is lower than 0.1%.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and in particular to a multi-threaded, low-power intelligent monitoring system based on an AI processor. Background Technology

[0002] With the deep application of artificial intelligence technology in the field of intelligent monitoring, existing systems mainly adopt three technical solutions: GPU computing solutions, traditional DSP processing solutions, and FPGA acceleration solutions. Although GPU solutions have strong computing power, reaching several TOPS, their typical power consumption is as high as 15-30W, and the hardware cost is high, making it difficult to meet the needs of large-scale deployment. DSP solutions (such as TI chips) have lower power consumption (5-10W), but insufficient computing power, usually <4 TOPS, which can only support basic behavior recognition of a single video stream. Although FPGA solutions have programmability, the development cycle is long (6-8 months) and requires the support of a professional algorithm team, which leads to a sharp increase in system complexity. Therefore, the main problems with existing technologies are:

[0003] 1. Imbalance between computing power and power consumption: The performance ratios of GPU and DSP solutions are polarized, making it impossible to meet the dual requirements of "high-concurrency analysis" and "low-power operation" in intelligent monitoring scenarios.

[0004] 2. Fixed hardware dependence: Most solutions rely on dedicated AI acceleration hardware, such as GPU accelerator cards, which increases the cost of hardware replacement by 30%-50% when the device is iterated.

[0005] 3. Insufficient edge-side optimization: Traditional methods lack optimization strategies such as quantization and batch processing in NPU acceleration scenarios. Resource utilization is less than 60% when scheduling multiple tasks, and end-to-end latency exceeds 500ms. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-threaded, low-power intelligent monitoring system based on an AI processor, which effectively solves the deficiencies of the prior art.

[0007] To achieve the above objectives, one embodiment of the present invention provides a multi-threaded, low-power intelligent monitoring system based on an AI processor, including an RK3588 processor, a multi-threaded task scheduling module, a model quantization module, a batch processing optimization unit, a zero-copy video stream processing architecture, an energy efficiency control module, a multi-model collaborative inference engine, and a frame ID synchronization unit. The RK3588 processor integrates an NPU module and a multi-core CPU architecture. The NPU module shares memory space with the multi-core CPU via a bus. The multi-threaded task scheduling module is communicatively connected to both the NPU module and the multi-core CPU. The multi-threaded task scheduling module is configured to receive at least eight video stream data streams and generate priority queues based on task types. The multi-threaded task scheduling module distributes AI inference tasks to the NPU module through a dynamic thread pool. The model quantization module is deployed on the multi-core CPU and data-connected to the NPU module. The model quantization module is used to convert FP32 neural network models into INT8 quantized models and transfer the quantized model parameters to the dedicated memory area of ​​the NPU via a DMA channel. The batch processing optimization unit is embedded in the NPU driver layer and connected to the output of the model quantization module. The optimization unit dynamically adjusts the batch processing size of the NPU, based on real-time monitoring of NPU computing resource utilization and task queue depth. The zero-copy video stream processing architecture includes a camera interface module and an NPU input buffer. The camera interface module directly transmits video stream data to the NPU input buffer via physical memory mapping, bypassing CPU memory transfer. The energy efficiency control module is electrically connected to the voltage and frequency adjustment unit of the NP module. The energy efficiency control module dynamically switches the NPU's operating mode to high-performance mode or low-power mode based on the task load rate fed back by the multi-threaded task scheduling module. The multi-model collaborative inference engine runs on the heterogeneous computing core of the NPU and is connected to the output of the batch processing optimization unit. The multi-model collaborative inference engine supports parallel inference of face recognition model, behavior analysis model, and license plate detection model, and the inference results of each model interact through a shared memory pool. The frame ID synchronization unit is communicatively connected to the camera interface module and the multi-model collaborative inference engine. The frame ID synchronization unit adds a unique identifier to each frame of video data based on a hardware timestamp generator and aligns the inference results with the original video frames in time and space through an asynchronous callback mechanism.

[0008] Preferably, in any of the above schemes, the multi-threaded task scheduling module includes a task receiving submodule, a task classifier, and a dynamic thread pool. The task receiving submodule is connected to the network protocol stack and video decoder and receives more than 8 H.264 video streams in parallel through multiplexing technology. The task classifier is connected to the output of the task receiving submodule. The task classifier divides tasks into real-time alarm tasks and regular detection tasks based on predefined rules and assigns the highest priority to real-time alarm tasks. The dynamic thread pool is bound to the output of the task classifier and the NPU computing core. The number of threads in the dynamic thread pool is equal to the number of parallel computing units of the NPU, and each thread occupies an NPU computing core.

[0009] Preferably, in any of the above solutions, the model quantization module collaborates with the NPU module in the following manner:

[0010] 1) During the model conversion phase, RKNN-Toolkit2 is used to convert the TensorFlow model into an NPU-specific instruction set, and inter-layer calibration nodes are inserted during the conversion process;

[0011] 2) During the inference execution phase, when the NPU module calls the quantized model parameters, it automatically activates the hardware-accelerated INT8 multiply-accumulate unit and skips the floating-point operation logic circuit.

[0012] Preferably, in any of the above schemes, the relationship between the batch processing optimization unit and the NPU computing resources is as follows:

[0013] When the NPU's MAC unit utilization is below 70%, the batch processing size is automatically increased to the maximum supported value;

[0014] When the task queue depth exceeds a preset threshold, a heterogeneous batch processing mechanism is activated, allowing video frames of different resolutions to be mixed and processed in the same computation cycle.

[0015] Preferably, in any of the above schemes, the camera DMA buffer and the NPU input buffer of the zero-copy video stream processing architecture share the same physical memory page and the address mapping is achieved through the IOMMU function of the memory management unit. The output of the video decoder of the zero-copy video stream processing architecture directly rotates the image orientation through a hardware-accelerated Rotator module.

[0016] Preferably, as described in any of the above schemes, the workflow of the ID synchronization unit includes the following steps:

[0017] S1. When capturing a video frame, the camera hardware triggers a timestamp generator to write a 64-bit microsecond-level identifier.

[0018] S2. When the inference result is returned, the timestamp identifier is quickly matched using a hash table, and the matching error is controlled within ±1 frame.

[0019] S3. When a match fails, start the circular buffer backtracking mechanism to retrieve the corresponding frame data from the cached frames of the most recent 5 seconds.

[0020] Preferably, as described in any of the above schemes, the adjustment strategy of the energy efficiency control module includes:

[0021] During low-load periods at night, the NPU clock frequency is reduced from 1.5GHz to 800MHz, and two redundant computing cores are shut down.

[0022] When the ambient temperature exceeds 55℃, the temperature-frequency closed-loop control algorithm is activated, and the NPU main frequency is reduced in a gradient of 0.1GHz / ℃.

[0023] In sudden high-load scenarios, the computing power can be boosted to the peak within 10ms through Dynamic Voltage Frequency Scaling (DVFS) technology.

[0024] Preferably, any of the above schemes further includes a distributed inference coordinator and a secure computing agent. The distributed inference coordinator and neighboring edge nodes form a mesh network through 5G modules. When the local NPU load exceeds 80%, it automatically offloads some tasks to the idle NPUs of neighboring nodes. The secure computing agent deploys an AES-256 encryption engine at the front end of the video stream input. The encrypted data is directly input into the NPU for ciphertext inference, and the decryption operation is only performed on authorized terminals.

[0025] The present invention has the following advantages:

[0026] 1. This multi-threaded low-power intelligent monitoring system based on an AI processor dynamically adjusts the NPU voltage and frequency through an energy efficiency control module. Combined with INT8 quantization and dynamic batch processing optimization, the system reduces power consumption to 8.2W in a scenario with 8 concurrent video streams, while maintaining a stable computing power of 6.8 TOPS. Furthermore, the hybrid precision quantization strategy reduces model computation by up to 60%, increases the utilization rate of the NPU's MAC unit to 92%, and the sleep mode switching technology reduces power consumption to as low as 0.5W during idle periods. The overall energy efficiency ratio reaches 0.83 TOPS / W, achieving synergistic optimization of low power consumption and high computing power.

[0027] 2. This AI processor-based multi-threaded low-power intelligent monitoring system, through dynamic thread pool management and strict binding with the NPU computing core, compresses the end-to-end latency of 8 video streams from 500ms to 180ms, with a latency jitter standard deviation of <15ms; through a heterogeneous batch processing mechanism, it supports mixed processing of frames with different resolutions, increasing throughput to 220FPS and NPU utilization from 60% to 89%; furthermore, through a zero-copy architecture, it reduces data copying time by 90% through direct DMA connection, reducing single-frame preprocessing time to 3ms, breaking through the real-time bottleneck of multi-task processing.

[0028] 3. This multi-threaded, low-power intelligent monitoring system based on an AI processor achieves 99.7% spatiotemporal alignment accuracy and a mismatch rate of <0.1% through a frame ID synchronization unit, avoiding the 15% recomputation overhead caused by frame loss in traditional solutions. Furthermore, it supports the parallel execution of three types of models through a multi-model collaborative inference engine, improving resource reuse by 40% and reducing memory usage by 35%. Through a distributed inference coordinator, the system maintains 85% NPU utilization in a 16-channel video stream scenario by task distribution, improving scalability by 3 times compared to a single-machine solution and enhancing the computational efficiency of edge-side optimization. Attached Figure Description

[0029] Figure 1 This is a structural diagram of the RK3588 monitoring system of the present invention;

[0030] Figure 2 This is a flowchart of the AI ​​task scheduling process of the present invention;

[0031] Figure 3 This is a block diagram of the NPU inference optimization of the present invention;

[0032] Figure 4 This is a timing diagram of frame ID association in this invention. Detailed Implementation

[0033] The present invention will be further described below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0034] like Figures 1 to 4As shown, a multi-threaded, low-power intelligent monitoring system based on an AI processor includes an RK3588 processor, a multi-threaded task scheduling module, a model quantization module, a batch processing optimization unit, a zero-copy video stream processing architecture, an energy efficiency control module, a multi-model collaborative inference engine, and a frame ID synchronization unit. The RK3588 processor integrates an NPU module and a multi-core CPU architecture. The NPU module shares memory space with the multi-core CPU via a bus. The multi-threaded task scheduling module communicates with both the NPU module and the multi-core CPU. The multi-threaded task scheduling module is configured to receive at least eight video streams and generate priority queues based on task type. It distributes AI inference tasks to the NPU module through a dynamic thread pool. The model quantization module is deployed on the multi-core CPU and is data-connected to the NPU module. The model quantization module converts FP32 neural network models into INT8 quantized models and transfers the quantized model parameters to the NPU's dedicated memory area via a DMA channel. The batch processing optimization unit is embedded in the NPU driver layer and connected to the output of the model quantization module. The batch processing optimization unit dynamically adjusts the NPU's... The batch processing size of the U is adjusted based on real-time monitoring of NPU computing resource utilization and task queue depth. The zero-copy video stream processing architecture includes a camera interface module and an NPU input buffer. The camera interface module directly transmits video stream data to the NPU input buffer through physical memory mapping, bypassing the CPU memory transfer. The energy efficiency control module is electrically connected to the voltage and frequency adjustment unit of the NP module. The energy efficiency control module dynamically switches the NPU's working mode to high-performance mode or low-power mode based on the task load rate fed back by the multi-threaded task scheduling module. The multi-model collaborative inference engine runs on the heterogeneous computing core of the NPU and is connected to the output of the batch processing optimization unit. The multi-model collaborative inference engine supports parallel inference of face recognition model, behavior analysis model and license plate detection model, and the inference results of each model are exchanged through a shared memory pool. The frame ID synchronization unit communicates with the camera interface module and the multi-model collaborative inference engine. The frame ID synchronization unit adds a unique identifier to each frame of video data based on a hardware timestamp generator and aligns the inference results with the original video frames in time and space through an asynchronous callback mechanism.

[0035] As an optional technical solution of the present invention, the multi-threaded task scheduling module includes a task receiving submodule, a task classifier, and a dynamic thread pool. The task receiving submodule is connected to the network protocol stack and video decoder and receives more than 8 H.264 video streams in parallel through multiplexing technology. The task classifier is connected to the output end of the task receiving submodule. The task classifier divides tasks into real-time alarm tasks and regular detection tasks based on predefined rules and assigns the highest priority to real-time alarm tasks. The dynamic thread pool is bound to the output end of the task classifier and the NPU computing core. The number of threads in the dynamic thread pool is equal to the number of parallel computing units of the NPU, and each thread occupies an NPU computing core.

[0036] As an optional technical solution of the present invention, this solution can be used.

[0037] Preferably, among the above solutions, the model quantization module collaborates with the NPU module in the following manner:

[0038] 1) During the model conversion phase, RKNN-Toolkit2 is used to convert the TensorFlow model into an NPU-specific instruction set, and inter-layer calibration nodes are inserted during the conversion process;

[0039] 2) During the inference execution phase, when the NPU module calls the quantized model parameters, it automatically activates the hardware-accelerated INT8 multiply-accumulate unit and skips the floating-point operation logic circuit.

[0040] As an optional technical solution of the present invention, this solution can be used.

[0041] Preferably, among the above schemes, the relationship between the batch processing optimization unit and the NPU computing resources is as follows:

[0042] When the NPU's MAC unit utilization is below 70%, the batch processing size is automatically increased to the maximum supported value;

[0043] When the task queue depth exceeds a preset threshold, a heterogeneous batch processing mechanism is activated, allowing video frames of different resolutions to be mixed and processed in the same computation cycle.

[0044] As an optional technical solution of the present invention, the camera DMA buffer and NPU input buffer of the zero-copy video stream processing architecture share the same physical memory page and the address mapping is realized through the IOMMU function of the memory management unit. The output of the video decoder of the zero-copy video stream processing architecture directly rotates the image orientation through the hardware-accelerated Rotator module.

[0045] As an optional technical solution of the present invention, the working process of the ID synchronization unit includes the following steps:

[0046] S1. When capturing a video frame, the camera hardware triggers a timestamp generator to write a 64-bit microsecond-level identifier.

[0047] S2. When the inference result is returned, the timestamp identifier is quickly matched using a hash table, and the matching error is controlled within ±1 frame.

[0048] S3. When a match fails, start the circular buffer backtracking mechanism to retrieve the corresponding frame data from the cached frames of the most recent 5 seconds.

[0049] As an optional technical solution of the present invention, the adjustment strategy of the energy efficiency control module includes:

[0050] During low-load periods at night, the NPU clock frequency is reduced from 1.5GHz to 800MHz, and two redundant computing cores are shut down.

[0051] When the ambient temperature exceeds 55℃, the temperature-frequency closed-loop control algorithm is activated, and the NPU main frequency is reduced in a gradient of 0.1GHz / ℃.

[0052] In sudden high-load scenarios, the computing power can be boosted to the peak within 10ms through Dynamic Voltage Frequency Scaling (DVFS) technology.

[0053] As an optional technical solution of the present invention, it further includes a distributed inference coordinator and a secure computing agent. The distributed inference coordinator and the neighboring edge nodes form a mesh network through 5G modules. When the local NPU load exceeds 80%, some tasks are automatically offloaded to the idle NPUs of neighboring nodes. The secure computing agent deploys an AES-256 encryption engine at the front end of the video stream input. The encrypted data is directly input into the NPU for ciphertext inference, and the decryption operation is only performed on authorized terminals.

[0054] Example 1: Implementation of Intelligent Traffic Monitoring Scenarios

[0055] Implementation details of the technical solution:

[0056] Hardware configuration: It adopts the RK3588 core board (NPU computing power 6TOPS), connects 4 4K MIPI cameras (3840×2160@30fps) and 2 1080P USB cameras, and accesses the traffic management platform through Gigabit Ethernet.

[0057] Multi-threaded scheduling: Create 6 worker threads corresponding to the 6 computing cores of the NPU, set the priority of the real-time task queue license plate recognition to level 3, and the priority of the regular task "traffic statistics" to level 1.

[0058] Model optimization:

[0059] Use RKNN-Toolkit2 to quantize the YOLOv5s model to INT8 format, while retaining the last 3 layers with INT16 precision.

[0060] The batch size can be dynamically adjusted from 8 to 32, and the batch size will be automatically expanded when the MAC utilization rate is less than 70%.

[0061] Zero-copy implementation:

[0062] The camera DMA buffer (physical address 0x7F800000-0x7FC00000) is directly mapped to the NPU input area;

[0063] A hardware Rotator module is used to achieve 90° image rotation, avoiding CPU intervention and saving 8ms / frame.

[0064] Actual test results:

[0065] When processing 6 video streams concurrently, the average power consumption is 9.3W, of which the NPU accounts for 6.8W, and the license plate recognition latency is 185ms±15ms.

[0066] Compared to the NVIDIA Jetson Xavier solution, which has a power consumption of 28W and a latency of 220ms, the energy efficiency ratio is improved by 3 times.

[0067] The NPU utilization rate is stable at 88%-92%, and it automatically switches to deep sleep mode (power consumption 0.6W) during low traffic periods at night.

[0068] Example 2: Implementation of Dynamic Optimization for Factory Safety Monitoring

[0069] Implementation details of the technical solution:

[0070] Heterogeneous task processing: Three types of models are run in parallel: face recognition (ResNet50-INT8), behavior detection (SlowFast-INT8), and device status recognition (custom CNN), and feature data is exchanged through a shared memory pool.

[0071] Dynamic energy efficiency control:

[0072] Set temperature threshold (55℃): When the NPU temperature is ≥50℃, activate the frequency reduction strategy (1.5GHz→1.2GHz).

[0073] Sudden load handling: When a 200% increase in task queue depth is detected within 5 seconds, the backup computing core is activated (power consumption increases from 8W to 10.5W).

[0074] Frame synchronization mechanism:

[0075] Each frame of video is marked using a hardware timestamp counter (1μs precision);

[0076] The circular buffer stores the most recent 300 frames (5 seconds of data), and the average time to initiate a backtracking query when a match fails is 2.3ms.

[0077] Security Extension Module:

[0078] The video stream is encrypted with AES-256 (256-bit key length, CBC mode) before being input into the NPU;

[0079] Establish TLS 1.3 encrypted channels between edge nodes with a transmission bandwidth utilization rate of <8%.

[0080] Actual test results:

[0081] In a high-temperature workshop environment (ambient temperature 45℃), the NPU worked continuously for 4 hours without frequency reduction (temperature stabilized at 53℃).

[0082] When 12 video streams are running concurrently, the behavior detection latency is 205ms, which is 56% faster than the traditional DSP solution (latency 480ms).

[0083] The accuracy loss of encryption inference is less than 0.5%, and the encryption / decryption throughput reaches 145fps, meeting the ISO / IEC27001 security standard.

[0084] In summary, this invention dynamically adjusts the NPU voltage and frequency through an energy efficiency control module, combined with INT8 quantization and dynamic batch processing optimization, reducing system power consumption to 8.2W in a scenario with 8 concurrent video streams, while maintaining a stable computing power of 6.8 TOPS. Furthermore, a hybrid precision quantization strategy reduces model computation by 60%, increases the NPU MAC unit utilization to 92%, and the sleep mode switching technology reduces idle power consumption to as low as 0.5W, achieving a comprehensive energy efficiency ratio of 0.83 TOPS / W. This achieves synergistic optimization of low power consumption and high computing power. The frame ID synchronization unit achieves 99.7% spatiotemporal alignment accuracy with a mismatch rate of <0.1%, avoiding the 15% recalculation overhead caused by frame loss in traditional solutions. Finally, a multi-model collaborative inference engine supports parallel execution of three types of models, providing... Source reuse rate is improved by 40%, and memory usage is reduced by 35%. Through task distribution via a distributed inference coordinator, the system maintains 85% NPU utilization in a 16-channel video stream scenario, achieving 3 times the scalability of a single-machine solution and improving the computational efficiency of edge-side optimization. By strictly binding dynamic thread pool management with the NPU computing core, the end-to-end latency of 8-channel video streams is compressed from 500ms to 180ms, with a latency jitter standard deviation of <15ms. Through a heterogeneous batch processing mechanism, mixed processing of frames with different resolutions is supported, increasing throughput to 220FPS and NPU utilization from 60% to 89%. Furthermore, through a zero-copy architecture and direct DMA connection, data copying time is reduced by 90%, and single-frame preprocessing time is reduced to 3ms, breaking through the real-time bottleneck of multi-task processing.

[0085] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-threaded, low-power intelligent monitoring system based on an AI processor, characterized in that: The system includes an RK3588 processor, a multi-threaded task scheduling module, a model quantization module, a batch processing optimization unit, a zero-copy video stream processing architecture, an energy efficiency control module, a multi-model collaborative inference engine, and a frame ID synchronization unit. The RK3588 processor integrates an NPU module and a multi-core CPU architecture. The NPU module shares memory space with the multi-core CPU via a bus. The multi-threaded task scheduling module is communicatively connected to both the NPU module and the multi-core CPU. The multi-threaded task scheduling module is configured to receive at least eight video streams and generate priority queues based on task type. It distributes AI inference tasks to the NPU module using a dynamic thread pool. The model quantization module is deployed on the multi-core CPU and is data-connected to the NPU module. It converts FP32 neural network models into INT8 quantized models and transfers the quantized model parameters to the NPU's dedicated memory area via a DMA channel. The batch processing optimization unit is embedded in the NPU driver layer and connected to the output of the model quantization module. This unit dynamically adjusts the batch processing size of the NPU. The size adjustment is based on real-time monitoring of NPU computing resource utilization and task queue depth. The zero-copy video stream processing architecture includes a camera interface module and an NPU input buffer. The camera interface module directly transmits video stream data to the NPU input buffer through physical memory mapping, bypassing the CPU memory transfer. The energy efficiency control module is electrically connected to the voltage and frequency adjustment unit of the NP module. The energy efficiency control module dynamically switches the NPU's working mode to high-performance mode or low-power mode based on the task load rate fed back by the multi-threaded task scheduling module. The multi-model collaborative inference engine runs on the heterogeneous computing core of the NPU and is connected to the output of the batch processing optimization unit. The multi-model collaborative inference engine supports parallel inference of face recognition model, behavior analysis model and license plate detection model, and the inference results of each model are exchanged through a shared memory pool. The frame ID synchronization unit is communicatively connected to the camera interface module and the multi-model collaborative inference engine. The frame ID synchronization unit adds a unique identifier to each frame of video data based on a hardware timestamp generator and aligns the inference results with the original video frames in time and space through an asynchronous callback mechanism.

2. The multi-threaded low-power intelligent monitoring system based on an AI processor according to claim 1, characterized in that: The multi-threaded task scheduling module includes a task receiving submodule, a task classifier, and a dynamic thread pool. The task receiving submodule is connected to the network protocol stack and video decoder and receives more than 8 H.264 video streams in parallel through multiplexing technology. The task classifier is connected to the output of the task receiving submodule. The task classifier divides tasks into real-time alarm tasks and regular detection tasks based on predefined rules and assigns the highest priority to real-time alarm tasks. The dynamic thread pool is bound to the output of the task classifier and the NPU computing core. The number of threads in the dynamic thread pool is equal to the number of parallel computing units of the NPU, and each thread occupies an NPU computing core.

3. The multi-threaded low-power intelligent monitoring system based on an AI processor according to claim 1, characterized in that: The model quantization module works in conjunction with the NPU module in the following ways: 1) During the model conversion phase, RKNN-Toolkit2 is used to convert the TensorFlow model into an NPU-specific instruction set, and inter-layer calibration nodes are inserted during the conversion process; 2) During the inference execution phase, when the NPU module calls the quantized model parameters, it automatically activates the hardware-accelerated INT8 multiply-accumulate unit and skips the floating-point operation logic circuit.

4. The multi-threaded low-power intelligent monitoring system based on an AI processor according to claim 1, characterized in that: The relationship between the batch processing optimization unit and the NPU computing resources is as follows: When the NPU's MAC unit utilization is below 70%, the batch processing size is automatically increased to the maximum supported value; When the task queue depth exceeds a preset threshold, a heterogeneous batch processing mechanism is activated, allowing video frames of different resolutions to be mixed and processed in the same computation cycle.

5. The multi-threaded low-power intelligent monitoring system based on an AI processor according to claim 1, characterized in that: The zero-copy video stream processing architecture's camera DMA buffer and NPU input buffer share the same physical memory page and achieve address mapping through the IOMMU function of the memory management unit. The output of the video decoder in the zero-copy video stream processing architecture directly rotates the image orientation through a hardware-accelerated Rotator module.

6. The multi-threaded low-power intelligent monitoring system based on an AI processor according to claim 1, characterized in that: The workflow of the ID synchronization unit includes the following steps: S1. When capturing a video frame, the camera hardware triggers a timestamp generator to write a 64-bit microsecond-level identifier. S2. When the inference result is returned, the timestamp identifier is quickly matched using a hash table, and the matching error is controlled within ±1 frame. S3. When a match fails, start the circular buffer backtracking mechanism to retrieve the corresponding frame data from the cached frames of the most recent 5 seconds.

7. The multi-threaded low-power intelligent monitoring system based on an AI processor according to claim 1, characterized in that: The adjustment strategy of the energy efficiency control module includes: During low-load periods at night, the NPU clock frequency is reduced from 1.5GHz to 800MHz, and two redundant computing cores are shut down. When the ambient temperature exceeds 55℃, the temperature-frequency closed-loop control algorithm is activated, and the NPU main frequency is reduced in a gradient of 0.1GHz / ℃. In sudden high-load scenarios, the computing power can be boosted to the peak within 10ms through Dynamic Voltage Frequency Scaling (DVFS) technology.

8. The multi-threaded low-power intelligent monitoring system based on an AI processor according to any one of claims 1-7, characterized in that: It further includes a distributed inference coordinator and a secure computing agent. The distributed inference coordinator and neighboring edge nodes form a mesh network through 5G modules. When the local NPU load exceeds 80%, it automatically offloads some tasks to the idle NPUs of neighboring nodes. The secure computing agent deploys an AES-256 encryption engine at the front end of the video stream input. The encrypted data is directly input into the NPU for ciphertext inference, and the decryption operation is only performed on authorized terminals.

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